1. Field of the Invention
The disclosure relates generally to complex event evaluation systems and methods, and, more particularly to systems and methods that evaluate critical level and priority of respective complex events.
2. Description of the Related Art
In event management systems such as network or medical event management systems, the occurrence of respective simplex events is monitored, and respective simplex events are filtered according to specific system predefined criteria. For example, in patient clinical monitoring systems, life status factors such as body temperature, breath speed, blood pressure, blood urea nitrogen, red blood cells (RBC) in urine, microalbuminuria, and others of a respective patient are monitored. If a respective factor exceeds its predefined upper and/or lower thresholds, a simplex event corresponding to the factor occurs.
All simplex events conforming to corresponding criteria are aggregated to generate one or several new events, called complex events. U.S. Pat. No. 6,336,139 discloses an event detection and aggregation mechanism in a distributed computing environment. Several criteria such as matching, duplicate, pass through, reset and threshold rules are provided for filtering simplex events. If simplex events conform to at least one of the criteria, the conformant simplex events are aggregated to generate at least one complex event for further processing. For example, if the body temperature, blood pressure, blood urea nitrogen, and RBC in urine of a patient are high, the patient may have diabetes. If the blood pressure and microalbuminuria of a patient are high, the patient may have hypertension. In the described examples, diabetes and hypertension are complex events aggregated by various factors (simplex events).
In addition to the direct influence of the occurrence of respective simplex events to complex events, the degree of influence of respective simplex events on complex events may be different. Further, the occurrence situation of respective simplex events may also directly influence the critical level of complex events. However, in the conventional systems, the aggregated complex events do not show the degree of influence of respective simplex events. That is the critical level of respective complex events cannot be determined. For example, if several patients have diabetes, the conventional clinical patient monitoring systems are unable to distinguish the degree of patient danger. Additionally, the priority of respective complex events cannot be known. For example, if two patients have diabetes and hypertension respectively, the conventional patient clinical monitoring systems are unable to determine an optimal processing order for patients. Further, because of differences in the individual constitutions of respective patients, for example the blood pressure of some patients may be always high, conventional systems may produce unreliable or erroneous judgments.